Book Image

Principles of Data Science

Book Image

Principles of Data Science

Overview of this book

Need to turn your skills at programming into effective data science skills? Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you’ll feel confident about asking—and answering—complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas. With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you’ll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You’ll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means.
Table of Contents (20 chapters)
Principles of Data Science
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Case study 1 – predicting stock prices based on social media


Our first case study will be quite exciting! We will attempt to predict the price of stock of a publically traded company using only social media sentiment. While this example will not use any explicit statistical/machine learning algorithms we will utilize EDA (exploratory data analysis) and use visuals in order to achieve our goal.

Text sentiment analysis

When talking about sentiment it should be clear what is meant. By sentiment, I am referring to a quantitative value (at the interval level) between -1 and 1. If the sentiment score of a text piece is close to -1, it is said to have negative sentiment. If the sentiment score is close to 1, then the text is said to have positive sentiment. If the sentiment score is close to 0, we say it has neutral sentiment. We will use a Python module called Textblob to measure our text sentiment:

# use the textblob module to make a function called stringToSentiment that returns a sentences sentiment...